You can use the following basic syntax to find the unique values in a column of a pandas DataFrame and then sort them:

df['my_column'].drop_duplicates().sort_values()

This will return a pandas Series that contains each unique value in a column sorted in ascending order.

To instead sort the unique values in descending order, use ascending=False:

df['my_column'].drop_duplicates().sort_values(ascending=False)

The following example shows how to use this syntax in practice.

Example: Find Unique Values in Pandas Column and Sort Them

Suppose we have the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'],
                   'points': [5, 5, 9, 12, 12, 5, 10, 13, 13, 19]})

#view DataFrame
print(df)

  team  points
0    A       5
1    A       5
2    A       9
3    A      12
4    A      12
5    B       5
6    B      10
7    B      13
8    B      13
9    B      19

We can use the following syntax to get the unique values from the points column and then sort them in ascending order:

#get unique values in points column and sort them
df['points'].drop_duplicates().sort_values()

0     5
2     9
6    10
3    12
7    13
9    19
Name: points, dtype: int64

The output displays each of the unique values in the points column sorted in ascending order:

• 5
• 9
• 10
• 12
• 13
• 19

We can also get the unique values in the points column sorted in descending order by specifying ascending=False within the sort_values() function:

#get unique values in points column and sort them in descending order
df['points'].drop_duplicates().sort_values(ascending=False)

9    19
7    13
3    12
6    10
2     9
0     5
Name: points, dtype: int64

The output displays each of the unique values in the points column sorted in descending order:

• 19
• 13
• 12
• 10
• 9
• 5

The following tutorials explain how to perform other common functions in pandas: